Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From China
The rapid development of Internet money funds (IMFs) may become the main development direction of money funds in the future. For the characteristics of IMFs return time series data with solid nonlinearity and poor smoothness, this study uses long and short-term memory (LSTM) neural network to predic...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10231332/ |
| _version_ | 1797688565479505920 |
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| author | Wang Tengxi |
| author_facet | Wang Tengxi |
| author_sort | Wang Tengxi |
| collection | DOAJ |
| description | The rapid development of Internet money funds (IMFs) may become the main development direction of money funds in the future. For the characteristics of IMFs return time series data with solid nonlinearity and poor smoothness, this study uses long and short-term memory (LSTM) neural network to predict IMFs return. By constructing a 1DCNN (one-dimensional convolutional neural network) and a self-attentive mechanism, the LSTM feature extraction capability is optimized, and an XGBOOST model is built after the output layer to construct a prediction error sequence to compensate for the original prediction sequence to achieve a correction effect. Finally, the trained model is applied to rolling forecast the 43-day return data of the actual trading days in the next two months, and the VaR method is applied to realize the IMF risk measure. The results displayed the following: (1) The 1DCNN-LSTM-SAM-XG has a significant improvement in accuracy compared with models such as LSTM neural network and SVR, and the MAPE values are reduced by 1.372% and 2.887%, respectively, indicating that the model established in this study is characterized by high accuracy and robustness. (2) According to the VaR methodology, the FUND series has the highest risk, the BANK series has the second highest risk, and the THIRD series has the lowest risk. |
| first_indexed | 2024-03-12T01:32:44Z |
| format | Article |
| id | doaj.art-eb285cbe632f47b2ad67e519a8cd27ff |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T01:32:44Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-eb285cbe632f47b2ad67e519a8cd27ff2023-09-11T23:01:19ZengIEEEIEEE Access2169-35362023-01-0111942059421710.1109/ACCESS.2023.330900710231332Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From ChinaWang Tengxi0https://orcid.org/0009-0000-1071-5251College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, ChinaThe rapid development of Internet money funds (IMFs) may become the main development direction of money funds in the future. For the characteristics of IMFs return time series data with solid nonlinearity and poor smoothness, this study uses long and short-term memory (LSTM) neural network to predict IMFs return. By constructing a 1DCNN (one-dimensional convolutional neural network) and a self-attentive mechanism, the LSTM feature extraction capability is optimized, and an XGBOOST model is built after the output layer to construct a prediction error sequence to compensate for the original prediction sequence to achieve a correction effect. Finally, the trained model is applied to rolling forecast the 43-day return data of the actual trading days in the next two months, and the VaR method is applied to realize the IMF risk measure. The results displayed the following: (1) The 1DCNN-LSTM-SAM-XG has a significant improvement in accuracy compared with models such as LSTM neural network and SVR, and the MAPE values are reduced by 1.372% and 2.887%, respectively, indicating that the model established in this study is characterized by high accuracy and robustness. (2) According to the VaR methodology, the FUND series has the highest risk, the BANK series has the second highest risk, and the THIRD series has the lowest risk.https://ieeexplore.ieee.org/document/10231332/Internet money fundsreturn rate forecasting1DCNN-LSTM-SAM-XGrisk assessmentVaR |
| spellingShingle | Wang Tengxi Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From China IEEE Access Internet money funds return rate forecasting 1DCNN-LSTM-SAM-XG risk assessment VaR |
| title | Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From China |
| title_full | Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From China |
| title_fullStr | Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From China |
| title_full_unstemmed | Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From China |
| title_short | Research on Forecasting and Risk Measurement of Internet Money Fund Returns Based on Error-Corrected 1DCNN-LSTM-SAM and VaR: Evidence From China |
| title_sort | research on forecasting and risk measurement of internet money fund returns based on error corrected 1dcnn lstm sam and var evidence from china |
| topic | Internet money funds return rate forecasting 1DCNN-LSTM-SAM-XG risk assessment VaR |
| url | https://ieeexplore.ieee.org/document/10231332/ |
| work_keys_str_mv | AT wangtengxi researchonforecastingandriskmeasurementofinternetmoneyfundreturnsbasedonerrorcorrected1dcnnlstmsamandvarevidencefromchina |